code: def test_log_torch_tensor_as_metric():
      torch_tensor_val = torch.rand(1)
      torch_tensor_float_val = float(torch_tensor_val[0])
  
      with start_run() as run:
          mlflow.log_metric("name_torch", torch_tensor_val)
  
      finished_run = tracking.MlflowClient().get_run(run.info.run_id)
      assert finished_run.data.metrics == {
          "name_torch": torch_tensor_float_val
      }
  
file_path: mlflow/tests/pytorch/test_pytorch_metric_value_conversion_utils.py
imports: import pytest
  import torch
  import mlflow
  from mlflow import tracking
  from mlflow.exceptions import INVALID_PARAMETER_VALUE, ErrorCode, MlflowException
  from mlflow.tracking.fluent import start_run
  from mlflow.tracking.metric_value_conversion_utils import convert_metric_value_to_float_if_possible
label_desc: Use image node_id 3 for calling a global function with example usage: test_log_torch_tensor_as_metric() without return types